TY  - JOUR
AU  - Agatonović-Kuštrin, Snežana
AU  - Hettiarachchi, Chandima G.
AU  - Morton, David W.
AU  - Ražić, Slavica
PY  - 2015
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2381
AB  - Health benefits of wine, especially with red wine, have been linked to the presence of a wide range of phenolic antioxidants. Thus, the aim of this study was to develop a simple, high performance thin layer chromatographic (HPTLC) method combined with high resolution digital plate images to visually compare multiple wine samples simultaneously on a single chromatographic plate and to quantify levels of gallic acid, caffeic acid, resveratrol and rutin, as representatives of the four different classes of phenolics found in wines. We also wanted to investigate the contribution of the investigated phenolic compounds to the total polyphenolic content (TPC) and total antioxidant capacity (TAC) of the wine samples. The average concentrations of caffeic acid, gallic acid, resveratrol, and rutin in the red wines were 2.15, 30.17, 0.59 and 2.47 mg/L respectively with their concentration below limit of quantification in the white wine samples. The highest concentration of resveratrol and rutin is found in the Cabernet and Shiraz wine samples. The amounts of gallic acid are correlated with TPC (r = 0.58). Italian wines have the highest correlation between TPC and TAC (r = 0.99) although they do not contain detectable amounts of resveratrol, they contain significant amount of rutin. Therefore, antioxidant properties might be associated with the presence of flavanols in these wines.
PB  - Elsevier Science BV, Amsterdam
T2  - Journal of Pharmaceutical and Biomedical Analysis
T1  - Analysis of phenolics in wine by high performance thin-layer chromatography with gradient elution and high resolution plate imaging
VL  - 102
SP  - 93
EP  - 99
DO  - 10.1016/j.jpba.2014.08.031
ER  - 

@article{
author = "Agatonović-Kuštrin, Snežana and Hettiarachchi, Chandima G. and Morton, David W. and Ražić, Slavica",
year = "2015",
abstract = "Health benefits of wine, especially with red wine, have been linked to the presence of a wide range of phenolic antioxidants. Thus, the aim of this study was to develop a simple, high performance thin layer chromatographic (HPTLC) method combined with high resolution digital plate images to visually compare multiple wine samples simultaneously on a single chromatographic plate and to quantify levels of gallic acid, caffeic acid, resveratrol and rutin, as representatives of the four different classes of phenolics found in wines. We also wanted to investigate the contribution of the investigated phenolic compounds to the total polyphenolic content (TPC) and total antioxidant capacity (TAC) of the wine samples. The average concentrations of caffeic acid, gallic acid, resveratrol, and rutin in the red wines were 2.15, 30.17, 0.59 and 2.47 mg/L respectively with their concentration below limit of quantification in the white wine samples. The highest concentration of resveratrol and rutin is found in the Cabernet and Shiraz wine samples. The amounts of gallic acid are correlated with TPC (r = 0.58). Italian wines have the highest correlation between TPC and TAC (r = 0.99) although they do not contain detectable amounts of resveratrol, they contain significant amount of rutin. Therefore, antioxidant properties might be associated with the presence of flavanols in these wines.",
publisher = "Elsevier Science BV, Amsterdam",
journal = "Journal of Pharmaceutical and Biomedical Analysis",
title = "Analysis of phenolics in wine by high performance thin-layer chromatography with gradient elution and high resolution plate imaging",
volume = "102",
pages = "93-99",
doi = "10.1016/j.jpba.2014.08.031"
}

Agatonović-Kuštrin, S., Hettiarachchi, C. G., Morton, D. W.,& Ražić, S.. (2015). Analysis of phenolics in wine by high performance thin-layer chromatography with gradient elution and high resolution plate imaging. in Journal of Pharmaceutical and Biomedical Analysis
Elsevier Science BV, Amsterdam., 102, 93-99.
https://doi.org/10.1016/j.jpba.2014.08.031

Agatonović-Kuštrin S, Hettiarachchi CG, Morton DW, Ražić S. Analysis of phenolics in wine by high performance thin-layer chromatography with gradient elution and high resolution plate imaging. in Journal of Pharmaceutical and Biomedical Analysis. 2015;102:93-99.
doi:10.1016/j.jpba.2014.08.031 .

Agatonović-Kuštrin, Snežana, Hettiarachchi, Chandima G., Morton, David W., Ražić, Slavica, "Analysis of phenolics in wine by high performance thin-layer chromatography with gradient elution and high resolution plate imaging" in Journal of Pharmaceutical and Biomedical Analysis, 102 (2015):93-99,
https://doi.org/10.1016/j.jpba.2014.08.031 . .

TY  - JOUR
AU  - Agatonović-Kuštrin, Snežana
AU  - Morton, David W.
AU  - Ražić, Slavica
PY  - 2014
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2137
AB  - Human activities have introduced tens of thousands of chemicals into water systems around the world which has significantly impacted water quality and aquatic ecosystems. The aim of this study was to develop an in silico QSAR model, capable of predicting the aquatic toxicity of pesticides in terms of a lethal dose (LD50) for fish without requiring the use of in vivo testing. A large data set of 230 diverse pesticides, including fungicides, herbicides and insecticides, with experimentally measured LD50 values was used to develop a predictive QSAR model. Each pesticide molecule was described using 62 calculated molecular descriptors. These descriptors were then related to the LD50 values via an Artificial Neural Network. Sensitivity analysis was used to select descriptors that best describe the model. The developed model included 13 molecular descriptors related to lipophilicity, hydrogen binding and polarity. Note the value of the predictive squared correlation coefficient (q(2)) for the final model was 0.748, demonstrating the model's predictability. In the domain of QSAR studies, a q(2) value above 0.5 renders a model to be predictive. The model could therefore be used to accurately screen a wide range of compounds without the need for actual compound synthesis and to prioritize potentially toxic compounds for further testing.
PB  - Bentham Science Publ Ltd, Sharjah
T2  - Combinatorial Chemistry & High Throughput Screening
T1  - In Silico Modelling of Pesticide Aquatic Toxicity
VL  - 17
IS  - 9
SP  - 808
EP  - 818
DO  - 10.2174/1386207317666141021110738
ER  - 

@article{
author = "Agatonović-Kuštrin, Snežana and Morton, David W. and Ražić, Slavica",
year = "2014",
abstract = "Human activities have introduced tens of thousands of chemicals into water systems around the world which has significantly impacted water quality and aquatic ecosystems. The aim of this study was to develop an in silico QSAR model, capable of predicting the aquatic toxicity of pesticides in terms of a lethal dose (LD50) for fish without requiring the use of in vivo testing. A large data set of 230 diverse pesticides, including fungicides, herbicides and insecticides, with experimentally measured LD50 values was used to develop a predictive QSAR model. Each pesticide molecule was described using 62 calculated molecular descriptors. These descriptors were then related to the LD50 values via an Artificial Neural Network. Sensitivity analysis was used to select descriptors that best describe the model. The developed model included 13 molecular descriptors related to lipophilicity, hydrogen binding and polarity. Note the value of the predictive squared correlation coefficient (q(2)) for the final model was 0.748, demonstrating the model's predictability. In the domain of QSAR studies, a q(2) value above 0.5 renders a model to be predictive. The model could therefore be used to accurately screen a wide range of compounds without the need for actual compound synthesis and to prioritize potentially toxic compounds for further testing.",
publisher = "Bentham Science Publ Ltd, Sharjah",
journal = "Combinatorial Chemistry & High Throughput Screening",
title = "In Silico Modelling of Pesticide Aquatic Toxicity",
volume = "17",
number = "9",
pages = "808-818",
doi = "10.2174/1386207317666141021110738"
}

Agatonović-Kuštrin, S., Morton, D. W.,& Ražić, S.. (2014). In Silico Modelling of Pesticide Aquatic Toxicity. in Combinatorial Chemistry & High Throughput Screening
Bentham Science Publ Ltd, Sharjah., 17(9), 808-818.
https://doi.org/10.2174/1386207317666141021110738

Agatonović-Kuštrin S, Morton DW, Ražić S. In Silico Modelling of Pesticide Aquatic Toxicity. in Combinatorial Chemistry & High Throughput Screening. 2014;17(9):808-818.
doi:10.2174/1386207317666141021110738 .

Agatonović-Kuštrin, Snežana, Morton, David W., Ražić, Slavica, "In Silico Modelling of Pesticide Aquatic Toxicity" in Combinatorial Chemistry & High Throughput Screening, 17, no. 9 (2014):808-818,
https://doi.org/10.2174/1386207317666141021110738 . .

TY  - JOUR
AU  - Agatonović-Kuštrin, Snežana
AU  - Morton, David W.
AU  - Truong, Lisa
AU  - Ražić, Slavica
PY  - 2014
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2124
AB  - A non-linear quantitative structure activity relationship (QSAR) model based on 350 drug molecules was developed as a predictive tool for drug protein binding, by correlating experimentally measured protein binding values with ten calculated molecular descriptors using a radial basis function (RBF) neural network. The developed model has a statistically significant overall correlation value (r > 0.73), a high efficiency ratio (0.986), and a good predictive squared correlation coefficient (q(2)) of 0.532, which is regarded as producing a robust and high quality QSAR model. The developed model may be used for the screening of drug candidate molecules that have high protein binding data, filtering out compounds that are unlikely to be protein bound, and may assist in the dose adjustment for drugs that are highly protein bound. The advantage of using such a model is that the percentage of a potential drug candidate that is protein bound (PB (%)) can be simply predicted from its molecular structure.
PB  - Bentham Science Publ Ltd, Sharjah
T2  - Combinatorial Chemistry & High Throughput Screening
T1  - Molecular Structural Characteristics Important in Drug-HSA Binding
VL  - 17
IS  - 10
SP  - 879
EP  - 890
UR  - https://hdl.handle.net/21.15107/rcub_farfar_2124
ER  - 

@article{
author = "Agatonović-Kuštrin, Snežana and Morton, David W. and Truong, Lisa and Ražić, Slavica",
year = "2014",
abstract = "A non-linear quantitative structure activity relationship (QSAR) model based on 350 drug molecules was developed as a predictive tool for drug protein binding, by correlating experimentally measured protein binding values with ten calculated molecular descriptors using a radial basis function (RBF) neural network. The developed model has a statistically significant overall correlation value (r > 0.73), a high efficiency ratio (0.986), and a good predictive squared correlation coefficient (q(2)) of 0.532, which is regarded as producing a robust and high quality QSAR model. The developed model may be used for the screening of drug candidate molecules that have high protein binding data, filtering out compounds that are unlikely to be protein bound, and may assist in the dose adjustment for drugs that are highly protein bound. The advantage of using such a model is that the percentage of a potential drug candidate that is protein bound (PB (%)) can be simply predicted from its molecular structure.",
publisher = "Bentham Science Publ Ltd, Sharjah",
journal = "Combinatorial Chemistry & High Throughput Screening",
title = "Molecular Structural Characteristics Important in Drug-HSA Binding",
volume = "17",
number = "10",
pages = "879-890",
url = "https://hdl.handle.net/21.15107/rcub_farfar_2124"
}

Agatonović-Kuštrin, S., Morton, D. W., Truong, L.,& Ražić, S.. (2014). Molecular Structural Characteristics Important in Drug-HSA Binding. in Combinatorial Chemistry & High Throughput Screening
Bentham Science Publ Ltd, Sharjah., 17(10), 879-890.
https://hdl.handle.net/21.15107/rcub_farfar_2124

Agatonović-Kuštrin S, Morton DW, Truong L, Ražić S. Molecular Structural Characteristics Important in Drug-HSA Binding. in Combinatorial Chemistry & High Throughput Screening. 2014;17(10):879-890.
https://hdl.handle.net/21.15107/rcub_farfar_2124 .

Agatonović-Kuštrin, Snežana, Morton, David W., Truong, Lisa, Ražić, Slavica, "Molecular Structural Characteristics Important in Drug-HSA Binding" in Combinatorial Chemistry & High Throughput Screening, 17, no. 10 (2014):879-890,
https://hdl.handle.net/21.15107/rcub_farfar_2124 .

TY  - JOUR
AU  - Loescher, Christine M.
AU  - Morton, David W.
AU  - Ražić, Slavica
AU  - Agatonović-Kuštrin, Snežana
PY  - 2014
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2093
AB  - Chromatography techniques such as HPTLC and HPLC are commonly used to produce a chemical fingerprint of a plant to allow identification and quantify the main constituents within the plant. The aims of this study were to compare HPTLC and HPLC, for qualitative and quantitative analysis of the major constituents of Calendula officinalis and to investigate the effect of different extraction techniques on the C officinalis extract composition from different parts of the plant. The results found HPTLC to be effective for qualitative analysis, however, HPLC was found to be more accurate for quantitative analysis. A combination of the two methods may be useful in a quality control setting as it would allow rapid qualitative analysis of herbal material while maintaining accurate quantification of extract composition. (C) 2014 Elsevier B.V. All rights reserved.
PB  - Elsevier Science BV, Amsterdam
T2  - Journal of Pharmaceutical and Biomedical Analysis
T1  - High performance thin layer chromatography (HPTLC) and high performance liquid chromatography (HPLC) for the qualitative and quantitative analysis of Calendula officinalis-Advantages and limitations
VL  - 98
SP  - 52
EP  - 59
DO  - 10.1016/j.jpba.2014.04.023
ER  - 

@article{
author = "Loescher, Christine M. and Morton, David W. and Ražić, Slavica and Agatonović-Kuštrin, Snežana",
year = "2014",
abstract = "Chromatography techniques such as HPTLC and HPLC are commonly used to produce a chemical fingerprint of a plant to allow identification and quantify the main constituents within the plant. The aims of this study were to compare HPTLC and HPLC, for qualitative and quantitative analysis of the major constituents of Calendula officinalis and to investigate the effect of different extraction techniques on the C officinalis extract composition from different parts of the plant. The results found HPTLC to be effective for qualitative analysis, however, HPLC was found to be more accurate for quantitative analysis. A combination of the two methods may be useful in a quality control setting as it would allow rapid qualitative analysis of herbal material while maintaining accurate quantification of extract composition. (C) 2014 Elsevier B.V. All rights reserved.",
publisher = "Elsevier Science BV, Amsterdam",
journal = "Journal of Pharmaceutical and Biomedical Analysis",
title = "High performance thin layer chromatography (HPTLC) and high performance liquid chromatography (HPLC) for the qualitative and quantitative analysis of Calendula officinalis-Advantages and limitations",
volume = "98",
pages = "52-59",
doi = "10.1016/j.jpba.2014.04.023"
}

Loescher, C. M., Morton, D. W., Ražić, S.,& Agatonović-Kuštrin, S.. (2014). High performance thin layer chromatography (HPTLC) and high performance liquid chromatography (HPLC) for the qualitative and quantitative analysis of Calendula officinalis-Advantages and limitations. in Journal of Pharmaceutical and Biomedical Analysis
Elsevier Science BV, Amsterdam., 98, 52-59.
https://doi.org/10.1016/j.jpba.2014.04.023

Loescher CM, Morton DW, Ražić S, Agatonović-Kuštrin S. High performance thin layer chromatography (HPTLC) and high performance liquid chromatography (HPLC) for the qualitative and quantitative analysis of Calendula officinalis-Advantages and limitations. in Journal of Pharmaceutical and Biomedical Analysis. 2014;98:52-59.
doi:10.1016/j.jpba.2014.04.023 .

Loescher, Christine M., Morton, David W., Ražić, Slavica, Agatonović-Kuštrin, Snežana, "High performance thin layer chromatography (HPTLC) and high performance liquid chromatography (HPLC) for the qualitative and quantitative analysis of Calendula officinalis-Advantages and limitations" in Journal of Pharmaceutical and Biomedical Analysis, 98 (2014):52-59,
https://doi.org/10.1016/j.jpba.2014.04.023 . .